Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value

Abstract

Demand response (DR) plays an essential role in power system management. To facilitate the implementation of these techniques, many aggregators have appeared in response as new mediating entities in the electricity market. These actors exploit the technologies to engage customers in DR programs, offering grid services like load scheduling. However, the growing number of aggregators has become a new challenge, making it difficult for utilities to manage the load scheduling problem. This paper presents a multi-agent reinforcement Learning (MARL) approach to a price-based DR program for multiple aggregators. A dynamic pricing scheme based on discounts is proposed to encourage residential customers to change their consumption patterns. This strategy is based on a cooperative framework for a set of DR Aggregators (DRAs). The DRAs take advantage of a reward offered by a Distribution System Operator (DSO) for performing a peak-shaving over the total system aggregated demand. Furthermore, a Shapley-Value-based reward sharing mechanism is implemented to fairly determine the individual contribution and calculate the individual reward for each DRA. Simulation results verify the merits of the proposed model for a multi-aggregator system, improving DRAs’ pricing strategies considering the overall objectives of the system. Consumption peaks were managed by reducing the Peak-to-Average Ratio (PAR) by 15%, and the MARL mechanism’s performance was improved in terms of reward function maximization and convergence time, the latter being reduced by 29%.